Sold by: MphasisÂ
Deployed on AWS
This solution is a deep learning based algorithm capable of training on transactional data, to identify spurious transactions.
Overview
This solution is a deep learning-based approach to learn and understand the patterns in financial transaction data. It aims at learning the normal behavior patterns of the transactions during the training process using a Restricted Boltzmann Machine algorithm. Once trained, the model can identify abnormal patterns of transactions, thereby classifying them as anomalous.
Highlights
- This solution can be used to identify transactions that are spurious given the usual transaction pattern of the customer. Identified spurious transactions can be flagged to the customer or blocked. This solution can be used by Banks, Credit Card Issuers etc.
- Data imbalance is a major challenge in the anomaly detection domain, with huge non-fraud data and limited fraudulent data. This solution uses a semi-supervised approach based generative deep learning model to learn normal transaction patterns using non-fraudulent data and then builds a 1-rule threshold model using data from both classes to identify the anomalous transactions using the inclusion-exclusion principle. The solution is also re-trainable to capture information drift.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input should have all columns in the train/test file except for "is_fraud" column.
- Limitations for input type
- Can predict on 1 input in the CSV only at a time only
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Financial-Transactions-Fraud-Detection/tree/main/infrence
https://github.com/Mphasis-ML-Marketplace/Financial-Transactions-Fraud-Detection/tree/main/infrence
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
all_columns | Input should have all columns in the train/test file except for "is_fraud" column. | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
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